Feature attention model attention for basic oxygen furnace endpoint temperature prediction

Accurate prediction of endpoint temperature in basic oxygen furnace (BOF) steelmaking is essential for production optimization and quality control. Traditional ensemble and deep learning methods rely on fixed-weight aggregation strategies that fail to adapt to operational heterogeneity in tabular steelmaking data. This study proposes a feature attention model attention (FAMA) framework enabling sample-adaptive fusion of heterogeneous base models through dual attention mechanisms. FAMA employs feature attention to extract high-dimensional representations and model attention to compute sample-adaptive fusion weights via cross-attention, directly evaluating model reliability in feature space and enabling differentiated fusion strategies. Experiments on 50000 industrial heats demonstrate that FAMA achieves mean absolute error (MAE) = 0.2173°C, root mean squared error (RMSE) = 1.1080°C, and coefficient of determination (R²) = 0.9795, reducing MAE by 89.9% compared to stacking baselines. Ablation studies confirm the synergistic effect of dual attention modules. Tests on abnormal conditions show that FAMA captures low-temperature abnormal patterns potentially associated with slopping-related heat-loss behavior through latent signal mining, achieving accurate predictions where traditional methods systematically overestimate temperatures. These results validate FAMA’s capability for intelligent steelmaking decision support and offer a novel paradigm for tabular data modeling.